Data Anonymization as a Vector Quantization Problem: Control over Privacy for Health Data
Abstract
This Paper Tackles the Topic of Data Anonymization from a Vector Quantization Point of View. the Admitted Goal in This Work is to Provide Means of Performing Data Anonymization to Avoid Single Individual or Group Re-Identification from a Data Set, While Maintaining as Much as Possible (And in a Very Specific Sense) Data Integrity and Structure. the Structure of the Data is First Captured by Clustering (With a Vector Quantization Approach), and We Propose to Use the Properties of This Vector Quantization to Anonymize the Data. under Some Assumptions over Possible Computations to Be Performed on the Data, We Give a Framework for Identifying and "Pushing Back Outliers in the Crowd", in This Clustering Sense, as Well as Anonymizing Cluster Members While Preserving Cluster-Level Statistics and Structure as Defined by the Assumptions (Density, Pairwise Distances, Cluster Shape and Members…).
Recommended Citation
Y. Miche et al., "Data Anonymization as a Vector Quantization Problem: Control over Privacy for Health Data," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9817 LNCS, pp. 193 - 203, Springer, Jan 2016.
The definitive version is available at https://doi.org/10.1007/978-3-319-45507-5_13
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-331945506-8
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2016
